library(tidyverse) # for graphing and data cleaning
library(gardenR) # for Lisa's garden data
library(lubridate) # for date manipulation
library(ggthemes) # for even more plotting themes
library(geofacet) # for special faceting with US map layout
theme_set(theme_minimal()) # My favorite ggplot() theme :)
# Lisa's garden data
data("garden_harvest")
# Seeds/plants (and other garden supply) costs
data("garden_spending")
# Planting dates and locations
data("garden_planting")
# Tidy Tuesday data
kids <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-09-15/kids.csv')
Before starting your assignment, you need to get yourself set up on GitHub and make sure GitHub is connected to R Studio. To do that, you should read the instruction (through the “Cloning a repo” section) and watch the video here. Then, do the following (if you get stuck on a step, don’t worry, I will help! You can always get started on the homework and we can figure out the GitHub piece later):
keep_md: TRUE in the YAML heading. The .md file is a markdown (NOT R Markdown) file that is an interim step to creating the html file. They are displayed fairly nicely in GitHub, so we want to keep it and look at it there. Click the boxes next to these two files, commit changes (remember to include a commit message), and push them (green up arrow).Put your name at the top of the document.
For ALL graphs, you should include appropriate labels.
Feel free to change the default theme, which I currently have set to theme_minimal().
Use good coding practice. Read the short sections on good code with pipes and ggplot2. This is part of your grade!
When you are finished with ALL the exercises, uncomment the options at the top so your document looks nicer. Don’t do it before then, or else you might miss some important warnings and messages.
These exercises will reiterate what you learned in the “Expanding the data wrangling toolkit” tutorial. If you haven’t gone through the tutorial yet, you should do that first.
garden_harvest data to find the total harvest weight in pounds for each vegetable and day of week (HINT: use the wday() function from lubridate). Display the results so that the vegetables are rows but the days of the week are columns.garden_harvest %>%
mutate(wk_day = wday(date, label = TRUE)) %>%
group_by(vegetable, wk_day) %>%
summarise(total_weight = sum(weight)) %>%
pivot_wider(id_cols = vegetable,
names_from = wk_day,
values_from = total_weight)
garden_harvest data to find the total harvest in pound for each vegetable variety and then try adding the plot from the garden_planting table. This will not turn out perfectly. What is the problem? How might you fix it?The problem is that we summarize the data so there is only one entry for each variety but we are trying to combine it with a dataset that has multiple entries for each variety. The final product has repeat entries for variety where the total harvest weight for the variety is the same, but the plot is different. We could modify this by summarizing the garden_planting data to only include the first plot for each variety and then combining the two datasets.
garden_harvest %>%
group_by(variety) %>%
summarize(variety_weight_lbs = sum(weight) * 0.00220462) %>%
left_join(garden_planting,
by = c("variety"))
I would like to understand how much money I “saved” by gardening, for each vegetable type. Describe how I could use the garden_harvest and garden_spending datasets, along with data from somewhere like this to answer this question. You can answer this in words, referencing various join functions. You don’t need R code but could provide some if it’s helpful.
Subset the data to tomatoes. Reorder the tomato varieties from smallest to largest first harvest date. Create a barplot of total harvest in pounds for each variety, in the new order.
garden_harvest %>%
filter(vegetable == "tomatoes") %>%
mutate(variety2 = fct_reorder(variety, date, min, .desc =FALSE)) %>%
group_by(variety2) %>%
summarise(variety_weight_lbs = sum(weight) * 0.00220462,
mindate=min(date)) %>% #I have this here just to make sure my reorder fn is working properly
ggplot() +
geom_col(aes(y = variety_weight_lbs, x = variety2)) +
labs(title = "Weight (lbs) of Tomato Harvests Ordered by Earliest to Latest Harvest Date",
x = "",
y= "") +
theme(axis.text.x = element_text(angle = 30))
garden_harvest data, create two new variables: one that makes the varieties lowercase and another that finds the length of the variety name. Arrange the data by vegetable and length of variety name (smallest to largest), with one row for each vegetable variety. HINT: use str_to_lower(), str_length(), and distinct().garden_harvest %>%
mutate(str_variety= str_to_lower(variety),
len_variety = str_length(variety)) %>%
distinct(str_variety, .keep_all = TRUE) %>%
arrange(vegetable, len_variety)
garden_harvest data, find all distinct vegetable varieties that have “er” or “ar” in their name. HINT: str_detect() with an “or” statement (use the | for “or”) and distinct().garden_harvest %>%
distinct(variety) %>%
mutate(has_er_ar = str_detect(variety, "er") | str_detect(variety, "ar"))
In this activity, you’ll examine some factors that may influence the use of bicycles in a bike-renting program. The data come from Washington, DC and cover the last quarter of 2014.
{300px}
{300px}
Two data tables are available:
Trips contains records of individual rentalsStations gives the locations of the bike rental stationsHere is the code to read in the data. We do this a little differently than usualy, which is why it is included here rather than at the top of this file. To avoid repeatedly re-reading the files, start the data import chunk with {r cache = TRUE} rather than the usual {r}.
data_site <-
"https://www.macalester.edu/~dshuman1/data/112/2014-Q4-Trips-History-Data.rds"
Trips <- readRDS(gzcon(url(data_site)))
Stations<-read_csv("http://www.macalester.edu/~dshuman1/data/112/DC-Stations.csv")
NOTE: The Trips data table is a random subset of 10,000 trips from the full quarterly data. Start with this small data table to develop your analysis commands. When you have this working well, you should access the full data set of more than 600,000 events by removing -Small from the name of the data_site.
It’s natural to expect that bikes are rented more at some times of day, some days of the week, some months of the year than others. The variable sdate gives the time (including the date) that the rental started. Make the following plots and interpret them:
sdate. Use geom_density().This plot shows that rentals are a lot higher in October and November and there is also a smaller peak in mid December.
ggplot(Trips) +
geom_density(aes(x = sdate)) +
labs(title = "Density of Rental Start Dates",
x = "",
y = "")
mutate() with lubridate’s hour() and minute() functions to extract the hour of the day and minute within the hour from sdate. Hint: A minute is 1/60 of an hour, so create a variable where 3:30 is 3.5 and 3:45 is 3.75.People start their rentals most often around 8 in the morning and even moreso around 5 or 6 in the evening.
Trips %>%
mutate(hr = hour(sdate),
minute = minute(sdate),
hr_min = hr + minute * 1/60) %>%
ggplot() +
geom_density(aes(x = hr_min)) +
labs(title = "Density of Rental Start Times",
x = "",
y = "")
The rentals are relatively consistent over the weekend but there are noticeably fewer on the weekends.
Trips %>%
mutate(wk_day = wday(sdate, label = TRUE)) %>%
ggplot()+
geom_bar(aes(y = wk_day)) +
labs(title = "Number of Rides by Day of the Week",
x="",
y="")
The weekdays look the same with a spike in rentals in the morning and afternoon. However, on the weekend there is more of a bell curve and there is only one peak in rental times at about midday.
Trips %>%
mutate(hr = hour(sdate),
minute = minute(sdate),
hr_min = hr + minute * 1/60,
wk_day = wday(sdate, label = TRUE)) %>%
ggplot() +
facet_wrap(~wk_day)+
geom_density(aes(x = hr_min)) +
labs(title = "Density of Rental Start Times",
x = "",
y = "")
The variable client describes whether the renter is a regular user (level Registered) or has not joined the bike-rental organization (Casual). The next set of exercises investigate whether these two different categories of users show different rental behavior and how client interacts with the patterns you found in the previous exercises.
fill aesthetic for geom_density() to the client variable. You should also set alpha = .5 for transparency and color=NA to suppress the outline of the density function.It looks like for registered clients the same relationship is true on weekends and weekdays. During the weekdays, casual renters are renting bikes at midday mostly, instead of the two peaks.
Trips %>%
mutate(hr = hour(sdate),
minute = minute(sdate),
hr_min = hr + minute * 1/60,
wk_day = wday(sdate, label = TRUE)) %>%
ggplot() +
facet_wrap(~wk_day)+
geom_density(aes(x = hr_min, fill = client, alpha = 0.5), color = NA) +
labs(title = "Density of Rental Start Times",
x = "",
y = "")
position = position_stack() to geom_density(). In your opinion, is this better or worse in terms of telling a story? What are the advantages/disadvantages of each?It is easier to see the distinct differences in shape of the casual versus registered clients, but on first glance it also looks like there are significantly more casual riders than registered riders which is misleading. You have to look really carefully to understand that that isn’t true, so I prefer the previous plots.
Trips %>%
mutate(hr = hour(sdate),
minute = minute(sdate),
hr_min = hr + minute * 1/60,
wk_day = wday(sdate, label = TRUE)) %>%
ggplot() +
facet_wrap(~wk_day)+
geom_density(aes(x = hr_min, fill = client, alpha = 0.5), color = NA, position = position_stack()) +
labs(title = "Density of Rental Start Times",
x = "",
y = "")
position = position_stack()). Add a new variable to the dataset called weekend which will be “weekend” if the day is Saturday or Sunday and “weekday” otherwise (HINT: use the ifelse() function and the wday() function from lubridate). Then, update the graph from the previous problem by faceting on the new weekend variable.This plot shows that on the weekdays the registered riders are renting at two peaks, the beginning and end of the day compared to casual riders who are mostly renting midday. On the weekends, both groups peak at midday.
Trips %>%
mutate(hr = hour(sdate),
minute = minute(sdate),
hr_min = hr + minute * 1/60,
wk_day = wday(sdate, label = TRUE),
weekend = ifelse(wk_day == "Sat" | wk_day == "Sun", "weekend", "weekday")) %>%
ggplot() +
facet_wrap(~weekend) +
geom_density(aes(x = hr_min, fill = client, alpha = 0.5), color = NA) +
labs(title = "Density of Rental Start Times",
x = "",
y = "")
client and fill with weekday. What information does this graph tell you that the previous didn’t? Is one graph better than the other?This plot makes it easier to compare registered riders on the weekdays versus weekends and same for casual riders. It is easier to compare within a client group than the day of the week, but this plot doesn’t necessarily show any different or new information.
Trips %>%
mutate(hr = hour(sdate),
minute = minute(sdate),
hr_min = hr + minute * 1/60,
wk_day = wday(sdate, label = TRUE),
weekend = ifelse(wk_day == "Sat" | wk_day == "Sun", "weekend", "weekday")) %>%
ggplot() +
facet_wrap(~client) +
geom_density(aes(x = hr_min, fill = weekend, alpha = 0.5), color = NA) +
labs(title = "Density of Rental Start Times",
x = "",
y = "")
Stations to make a visualization of the total number of departures from each station in the Trips data. Use either color or size to show the variation in number of departures. We will improve this plot next week when we learn about maps!I don’t think this code is very efficient…
Trips %>%
group_by(sstation) %>%
summarize(departures = n(),
name = sstation) %>%
left_join(Stations,
by = c("name")) %>%
distinct(sstation, .keep_all = TRUE) %>%
ggplot() +
geom_point(aes(x = long, y = lat, size = departures)) +
labs(title = "Departures from each station spacially",
x = "Longitude",
y = "Latitude")
I notice that in the southeast corner where there is a cluster of a lot of stations, those are mostly registered users but there are a few stations on the outside that are almost completely casual users.
Trips %>%
mutate(casual = ifelse(client == "Casual",1, 0)) %>%
group_by(sstation) %>%
summarize(departures = n(),
name = sstation,
total_cas = sum(casual),
percent_cas = total_cas/departures * 100) %>%
left_join(Stations,
by = c("name")) %>%
distinct(sstation, .keep_all = TRUE) %>%
ggplot() +
geom_point(aes(x = long, y = lat, color = percent_cas)) +
labs(title = "Departures from each station spacially",
x = "Longitude",
y = "Latitude")
as_date(sdate) converts sdate from date-time format to date format.TopDepartures <- Trips %>%
mutate(new_date = as_date(sdate)) %>%
group_by(sstation, new_date) %>%
count() %>%
arrange(desc(n)) %>%
head(n=10)
TopDepartures
Trips %>%
mutate(new_date = as_date(sdate)) %>%
inner_join(TopDepartures,
by = c("sstation", "new_date"))
This shows that the majority of casual riders are riding ont the weekends whereas the bulk of the registered riders are using during the weekdays.
Trips %>%
mutate(new_date = as_date(sdate)) %>%
inner_join(TopDepartures,
by = c("sstation", "new_date")) %>%
mutate(wk_day = wday(new_date, label = TRUE)) %>%
group_by(client, wk_day) %>%
count() %>%
mutate(client_prop = ifelse(client == "Casual", n/29, n/49)) %>%
pivot_wider(id_cols = -n,
names_from = wk_day,
values_from = client_prop,
names_sort = TRUE)
DID YOU REMEMBER TO GO BACK AND CHANGE THIS SET OF EXERCISES TO THE LARGER DATASET? IF NOT, DO THAT NOW.
This problem uses the data from the Tidy Tuesday competition this week, kids. If you need to refresh your memory on the data, read about it here.
facet_geo(). The graphic won’t load below since it came from a location on my computer. So, you’ll have to reference the original html on the moodle page to see it.kids %>%
filter(variable == "lib") %>%
filter(year == 1997 | year == 2016) %>%
ggplot() +
facet_geo(~ state) +
geom_line(aes(x = year, y = inf_adj_perchild)) +
labs(title = "Change in public spending on libraries from 1997 to 2016")
DID YOU REMEMBER TO UNCOMMENT THE OPTIONS AT THE TOP?